Bucky Moore: The Next Decade of AI Infrastructure

20:50But I think it's still really early. You know, another thing that I've been thinking about a lot the past couple of days, not weeks, not months, is, you know, in Apple's announcement at WWDC the other day, they revealed that now, they're going to have a platform in iOS whereby developers can take advantage of on-device models that Apple provides, totally free inference on-device. How does that throw a wrench or how will solutions like that throw a wrench into the dynamic you just laid out? Yeah. So I think the on-device stuff is super interesting. And at Lightspeed, we have a company called Cartesia that's doing some really amazing work there.

21:24The way I'd put it is that today there are still a lot of use cases where the quality of an on-device model that you can serve is just not good enough in terms of its performance and the level of intelligence that you can deliver to your users to make it worth skipping the call back to some bigger model that's on some server in someone's cloud, right? So for example, in CodeGen, you're still seeing a lot of use of large frontier models rather than local models for that reason, right? But on the other hand, if you think about, say, every mobile device in the world, whether it's an Apple device or a Samsung device or what have you, having some kind of AI assistant that has really robust audio and speech capabilities that you can kind of communicate with in that way.

22:01I can think of all kinds of reasons why in the near term, we're going to see on-device models serve those use cases where latency is really, really key and performance is good enough with the scale and quality of model you can put on device. So I think it's an exciting paradigm. I think it's going to likely be its own kind of like, I don't want to say niche, but let's just say like, you know, kind of discrete market. Yeah. And I think that the Frontier Labs could care less about that market right now, as far as I can tell, because their whole thing is about building these bigger models that can deliver a maximum amount of intelligence and kind of deliver on those frontier-like properties.

22:34That's a little bit diametrically opposed to like, how do we shrink this thing down and kind of constrain it to the environment that say a phone or a laptop represents? So my point is to say there that I think it's a really interesting pocket of white space for companies to actually go and play in an area where I don't anticipate the frontier labs being as aggressive. That could change. But as far as like how they seem to have, how it seems to align with their commercial objectives, it's a lot less clear to me that the frontier labs are going to be able to They're going to want to play that game because they're just so focused on pushing the state of the art and scale and performance.

23:01Yeah, that makes sense. OK, so so we have open source, closed source models and local local models. I think you were going to get to another primitive that I cut you off of. Yeah, I mean, I think there's this this really exciting new scaling paradigm in the form of test time compute and reinforcement learning that is emerging. And I think what you're starting to see when you go and talk to folks at the Frontier Labs is they'll tell you that they're quote unquote infrastructure constrained. And they don't mean that they don't have enough GPUs when they say that. What they mean is that there is a set of infrastructure that one has to build, maintain, and scale out in order to do reinforcement learning at really large scale on these tasks like programming or math or perhaps even more qualitative tasks that have less clear reward models to them that you have to build environments to actually simulate so that you can do the reinforcement learning on.

23:46And so an example of this would be like, suppose you wanted to train an agent that does offensive security. So you want to train an agent to behave like a hacker, right? Well, in order to train that agent to understand how to hack, you have to show it what it means to hack and you have to help it find vulnerabilities and exploit those vulnerabilities. And to do that, you have to spin up a bunch of servers and you have to put vulnerabilities on those servers and you have to create an environment that represents sort of the real world use case that you hope this agent can thrive in. And so the ball of wax there in terms of creating these arbitrary environments where agents can actually learn in a practical and scalable way is very hard.

24:23And so one area that we're starting to see a lot of innovation in is, well, what does it mean to actually create these environments? And what does it mean to maintain these environments? And what does it mean to ensure that the results you're getting from the agent playing and learning in these environments are good results? And so I think this whole area of effectively simulation environments for reinforcement learning is very fertile ground and an area that I think we're excited about as a firm and certainly is an area that a lot of really smart people in the industry are spending time building in.

24:49So I'd say that's a big area where I think if you believe that RL is sort of the next scaling paradigm, say post-pre-training and post-test time compute, what you're going to start to see very quickly is that the infrastructure around doing RL and making it easy and easily accessible for enterprises is going to be really, really critical. So that's an area that I think is really exciting. And while we're on the topic of agents, I think it goes without saying, if we just look at even this most recent Y Combinator batch, the number of companies that are effectively making a bet that there will be agents all around us and those agents will need infrastructure and tools and all kinds of primitives to do their job in the way that we do as humans is pretty clear.

25:23And so, you know, everything from like, what browser does the agent use when it needs to use the web to how does it connect to tools on your computer and the cloud to how do you bring data from third party applications into the agent? These are all I think, like really needy problems that I suspect we'll see companies get built in and the winners be very successful as and valuable as companies in. So that's another area that I'm excited about is just like this world where agents are doing more and more of the work, not just how do you teach them to do the work as I was talking about earlier, but then how do you give them the tools and the infrastructure they need to actually carry it out, right?

25:54So that's another really exciting area that I think a lot about. And I think you're seeing a lot of investment dollars flow into and a lot of good entrepreneurial activity. What would you say some of the less obvious infra opportunities around agents are right now? Everyone's talking about MCP and, you know, connect, connecting models to, you know, infinite number of, you know, sources of data or like, what, what are some of the less obvious things that excite you in terms of agents? I think until it became clear to me how important this was, like these, building these simulation environments was certainly like a non-obvious one that seems very, very fundamental.

26:27So that, that's one. And I think if you're inside of one of the big frontier labs, it's a very obvious one, but outside of it, you just have a little bit less exposure to the, to the nitty gritty of like how you actually go about solving these problems. So that was one. Another one that I would point to is, I think what you're going to start to see is that these vertical software companies that effectively are AI native in shape, as well as those that weren't necessarily born in an AI native era, but are kind of bringing AI into their product. A lot of what they have to do is interact with these really like old, often on-premise pieces of software for their customers.

26:57And so if they want to build agentic flows, the question of how you connect those agents to like an on-premise EHR in a healthcare sense, or maybe a logistics management software that runs in a server in an office of like a trucking warehouse. Like these are actually really important problems if you think about enabling these agents to make their full impact in more esoteric industries where automation is super, super high value. So I've also been really interested in that, which is like, there's just all these interaction points that have existed for a really long time that were not built to accommodate agents.

27:25How do you enable them to do that in a way that kind of aligns with like, say, if you're a company like Like Samsara, they're customers who are all trucking companies, as an example, right? And so I think there's kind of this long tail of industries and businesses that have very, very old technology that they want to build automations over. And I think how you teach agents to actually engage with that technology is a really important and interesting problem. Yeah, I guess like if you're, if you're anthropic, you know, it's very obvious that you add a, you add a button to connect with the Google drive or the email or the calendar, but it's less obvious for how the trucking data gets into the model, right?

28:01Yeah. And I think if you're anthropic, you're focused on the, you know, sort of the most popular applications, right? Like the Salesforce is in the Slack. And we should talk about Slack too, given the recent news about the data sharing stuff. I think that's really interesting, right? And so for those listening to this that haven't seen the news, Salesforce very recently said they're going to start to be a little bit more restrictive with how companies can access the data inside of Slack. And the reason they did this was in the context of how people are using Slack data to build their AI systems, it's very, very valuable data.

28:29And Salesforce has ambitions to build some of that stuff themselves. And so they've decided that they want to give themselves an inherent advantage in terms of being able to wield that data in ways that vendors who might be building competitive products to what they have on their roadmap cannot. I think this is really interesting because it seems like there's a precedent that's going to be set by this. And I think it could go one of two ways. The first is that customers speak up and say, hey, this isn't cool. I want to make sure I have the ability to bring the best technology to bear on top of this data.

28:56And if I can't do that, that's a big problem for me. And then the other would be, and this is like sort of a Rorschach test for Salesforce's leverage with their customers, like Salesforce says no, and this is how it's going to be. And I worry that if the latter plays out, which I'll caveat, I don't think is very likely because I think in these situations, what's best for the customer tends to prevail, which is a good thing. But if it does play out, you can imagine Atlassian, you can imagine ServiceNow and other core systems of record to very quickly start to say, okay, we're going to do that too.

29:23And what would happen, I think, if that really played out that way, which I don't want to happen, is it would just be a very, very challenging environment for a lot of these AI application companies that rely on that data that their customers store in those systems to innovate the way that they want to. And so I'm watching this very closely. I'm curious what you think of it. Yeah, I find it fascinating. I mean, I've been following this trend on more of like the consumer side for a while. Obviously, you know, lots of big publishers aren't thrilled that their content is being, you know, scraped via rag and there are services out there that are helping them monetize it.

29:56And obviously we've seen things like Reddit, you know, saying, you know, locking their data and doing these one-off deals. It feels like this Slack Salesforce thing is like the first big shoe to drop more on the enterprise side. And it's different because these are businesses and companies where sort of the exchange of data is commonplace via APIs and things like that. And now it's like, nope, actually the walls are coming up. Yeah. Right. And so, yeah, I'm like super, super interested to see how this plays out. It also, for me, what it highlights is if you're a business that actually doesn't have your own data and you're like just you sort of run off of other people's data, like you're probably in a pretty uncomfortable spot right now because you're starting to wonder if like the data that you rely on might go away tomorrow.

30:42So, yeah, I don't know. I've been thinking about it as well nonstop since the news came out. Yeah, the Reddit analogy is interesting because I guess my knee-jerk reaction to that is, well, like if I'm a customer of Salesforce, like the data I store in Salesforce is mine, right? And I think it's contractually the case. I don't know the extent to which that's true, but that's my understanding. Whereas I think if it's Reddit, it's a much more global corpus. It's like user generated, but maybe not owned by the user. Whereas I think the messages in Slack are like owned by, say, Lightspeed in our case.

31:08And so it'll be really interesting to see how that plays things out. I mean, I don't know Reddit's terms of service, but, you know, I built a large user generated content platform, you know, many, many millions or however many hundreds or billions of hours of audio content. And I can tell you that the, you know, our terms were the users owned it. You know, our platform, we have the license to distribute it and, you know, to monetize it on behalf of the user. But the user owns typically the user owns user generated content. I don't know what's the case is with like a slack. I mean, on one hand, yeah, it's my I wrote the words, but it's sort of wrapped in a in a format that is Slack's own unique proprietary format.

31:48So I don't know. It's actually a very, very interesting question. And Slack's just one example. Right. Yeah. Yeah. I don't know. I think it's I think it's fascinating. We're going to it's we're about to see something. We're going to see sort of like a new era, I think, of the Internet and especially especially like enterprise software, I think. Totally agree. Talk to us about compute. Right. You did the Together deal when you're at Kleiner. There are a number of companies in the space that have gone after this opportunity. And I'm not talking about like NVIDIA. I'm talking about the startups that are reselling and provisioning and things like that.

32:20Talk to us about that space and how it's evolved over the past two years since ChatGPT came out and where it's going. So AI compute is a very, very interesting space to me on multiple levels. The first level that I think is interesting is just how misunderstood it is, right? I think there's this common term thrown around by the media to refer to these companies as GPU resellers. And while that is technically true, that they take delivery of GPUs that they either own or lease, and then they run software on them inside of data centers and then deliver that as a service to customers, I feel like if I just replaced GPUs with x86 servers, I could have described AWS, right?

32:54And so what's the fundamental difference between these companies that are known as GPU resellers by folks like the Information and AWS? AWS has had multiple decades now to build higher level APIs and services on top of that core offering. That is servers being resold by them, right? And so I think what's happening here is like these companies are sort of misunderstood and like not being treated like the cloud providers that they effectively are. Because they are just so early in their lifecycle that a lot of the innovation that got AWS to where it is today is very much underway. So like that's point one of why I think it's interesting is I think these companies are misunderstood.

33:27in the limit, the winning companies will look more like AWS or GCP than they will say a colo provider, which I think is sort of the pejorative way of putting reseller. So that's kind of point one. Point two is that I think there's like two really important modalities to talk about on compute, right? The first is training and the second is inference. So let's talk about training first, right? So, so training is typically skewing towards, I mean, obviously companies that are training models, which tend to be either frontier labs or very, very well-capitalized companies that are in, say, modalities that are non-overlapping with what the Frontier Labs are focused on, say, like audio, for example.

34:01And so in that space, typically customers are shopping for a large number of chips, like on the order of thousands, sometimes many thousands, number one. And number two, because of that, the number one heuristic that they're using to make the decision is like, how do I get the best possible price? The second would probably be, can I ensure that the chips that I'm bringing online with this partner, are they reliable, right? As in, when they go down, are they going to come back up? And can I get to the SLAs that I need to ensure that when I run my training jobs, I can run those training jobs without excessive failure.

34:27So I call that out because I think given it's such a price-driven market, the opportunity to actually differentiate with software today is relatively limited because if you're, say, SSI or you're OpenAI or any one of these other frontier labs that are buying tens of thousands of chips, you really just are trying to think about how to minimize capex exposure without necessarily having to compromise on the reliability of your access to those chips. So that's kind of modality want, right? And that's, I think, again, probably a little bit more commodity than not today. If you look across the landscape from the hyperscalers to new clouds, like a CoreWeaver or a Together AI, but still a very fast growing market and where the lion's share of the dollars are.

35:02Is there a startup opportunity though, if it's generally been commoditized at this point? I think the opportunity for startups is as that market becomes more software driven. I think that's where the opportunity really arises for startups to thrive, right? So if you talk about Together AI, for example, there is still a fairly meaningful layer of software that exists on the training side of things in the form of these custom CUDA kernels. So what I mean by custom CUDA kernels is for an NVIDIA GPU, it has this programming language on top of it that you use to program the GPU called CUDA, C-U-D-A.

35:32And when I refer to a kernel, what I mean is writing a custom software path of how the model actually interacts with that CUDA code in the GPU itself to make it faster and to get higher utilization out of the GPU. Because GPUs are very expensive, getting high utilization out of them is very, very lucrative for anyone using GPUs, right? So a company like Together AI, what they've really done is they've said, hey, we're gonna write a lot of these custom kernels, these custom pathways down to the GPU for companies that wanna train models, just like Anthropic and OpenAI have, right? Because a lot of the talent that actually understands how to do that, they're inside of the Frontier Labs.

36:04And the promise of Together is we've brought a bunch of those people together from Stanford and other key places and said, hey, we're gonna deliver that platform engineering service to everyone, right? So that's sort of like the layer of software that exists in training. Whereas by comparison on the inference side, the second workload, one, it's growing very fast, because intuitively more companies are saying, hey, I've trained models or I've figured out how to use other models and I want to put them in production and point them at my customers and my software. So that market's growing very, very fast.

36:31And what's interesting about it is, one, because it's more online, meaning you're embedding this inference endpoint in your product. You have kind of more concerns like you would in running any production software, which is like, How does it scale out? How do I ensure that it stays on if it fails? How do I monitor performance? How do I ensure that the accuracy of the model as I'm doing tricks to make the output tokens go faster isn't being compromised? So there are a lot of these kind of really interesting concerns that kind of fall into place where what you start to see is that if you're at OpenAire Anthropic, you've got a team of folks that work on the serving team, which is effectively the inference infrastructure that are making sure all this stuff works in a way that delivers a great customer experience.

37:07And so the opportunity for these next-gen players on the inference side by comparison to training is much meatier in terms of software, I would argue. And so what you're starting to see is that companies like Together AI or Fireworks or Base 10 are starting to emerge as leaders in this space and grow very, very fast because they've solved a lot of those operational pain points that exist when you have a custom model or an open source model that you want to serve in your application. And how much do the labs want to own of that workflow, the inference opportunity, be it open or closed, right? So like a Mistral or an OpenAI or an Anthropic.

37:41If you're in the business of serving customer open source models, you want to own all this, right? Because this is where the value accrues at the infrastructure layer, I would argue, right? Now, if you are selling a closed model a la OpenAI or Anthropic, for example, you have to solve all these problems yourself because your customers aren't looking for an inference runtime. They're looking for an endpoint that they can call and get answers back, right? They want to query your LLM and they want to get answers back. They don't want to have to worry about auto scaling. It's your job to make it work and stay on and be performant.

38:10And that's sort of what they pay you the big bucks for. And so I think if you're at a Frontier Lab, you don't want your customers to think about any of this. You want to own it all. And you want to own it all so that you can deliver the best possible customer experience. It's magic. It's behind the scenes. But if you're not a Frontier Lab and you're one of these companies that's in the inference business, what you really want is you want to enable other companies to deliver that experience to their customers without having the same inference and serving expertise that OpenAI or Anthropic or DeepMind has.

38:35Yeah, makes sense. Makes sense. We talked about LLMs, but let's just talk about models and sort of new architectures in general. You know, obviously the things we're talking about, they're all transformer based. As an infrastructure, as an infra investor, like how much do you think about the potential for new architectures? You know, I'm sure you've heard of some of these labs that are taking completely new approaches or attempting to take completely new approaches to the model layer. I mean, is that something you're thinking about? you're looking for, you're focused on? So I think in the research community, the reason why alternative architectures are very interesting is one, the frontier labs are betting so big on transformers and really mostly innovating around how you scale transformers and how you curate new data into it and more data into it, as well as compute is what I mean by scaling.

39:22That there is a bit of white space right now where it's like, well, what if one of these alternative architectures really, really turns out to be the way forward? Or what if blending one of these alternative architectures with transformers turns out to be the unlock that the industry hasn't seen before. And so I think what you're starting to see is that this is actually very fertile ground for startups. One, because the frontier labs aren't focused on it, like I said. And two, I think you're a bit less compute bound, meaning you don't necessarily have to kind of keep up with the big boys, so to speak, and buy 50 ,000 or 100 ,000 H200s or what have you to prove that an alternative architecture actually works in scales.

39:52So I think it's exciting in the sense that it's a bit less CapEx intensive than, say, going and training frontier transformers. in that sense. But on the other hand, I think it's still very early to say that any one of these alternative architectures is likely to reach some level of ubiquity or standardization the way a transformer has, right? And so an example of this would be like state space models, which a company called Cartesia is very known for taking forward. And I think what they found is that it unlocks some really, really interesting properties when it comes to use cases where you have long sequence legs on the input side.

40:24So you want to pass the model a lot of context and you care a lot about latency, right? So that's where these state-based models seem to be quite special. So what they've done is they've actually said, hey, like with that in mind, let's verticalize and let's actually build the best audio models and the best platform for developing audio agents and voice agents in the market. And so I think one instantiation of what alternative architectures could lead to is companies that find a way to verticalize in that sense. But then I think the other would be, hey, maybe one of these alternative architectures, if not SSMs, start to pay off in like a nonlinear way that makes sense to actually go and raise a bunch of money to scale them to the level that we're seeing LLMs being pushed to today?

40:58And I think that is an unanswered, but very interesting question as an infrastructure investor that you're thinking about. And the other thing I'd say is you're actually starting to see techniques like diffusion, which has become very popular on the image and video side, be applied to LLMs as well. So we're starting to see kind of cross-pollination of these techniques across different modalities in ways that I think, frankly, just lead to a more exciting range of outcomes. And I think it's good for everyone in the industry, and namely the users of this technology, to see that these experiments are being run because I think the guarantee is that in the end, developers and consumers of AI are going to get the best possible product.

41:30So you mentioned battery, obviously Kleiner you were at, now you're at Lightspeed. You've seen, you know, you've seen venture over the past, you know, chunk of years and you've seen it evolve. And now obviously you're at Lightspeed, which is a large platform. Like, give us your take on the state of venture, especially as it relates to AI. Who are the winners and losers? What's the best place to be from a venture standpoint? What's not a great place to be? And what's your outlook for the current sort of batch of funds, I would say? So I think there's a lot of conventional wisdoms that are thrown around in the context of this conversation that I actually believe are true, hence why they're conventional wisdoms.

42:06One would be companies are staying private longer, and so therefore they're going to raise more capital in the private markets and more of the returns are going to be generated as private companies. That's one. I think a second is that the scale of the opportunities in AI is larger than we've ever seen before. And so these companies are going to digest a lot more money, I think, from investors and require a lot more investor capital than companies previously. And I think as a consequence of that, you have more capital going into these companies and larger end outcomes. I think you could see a world in which venture returns actually at an industry level really start to look a lot better even than they have been in the past.

42:40And so that I think is pretty conventionally true. And I believe it deeply, which is I think we will see trillion dollar companies that go public as like multi-trillion dollar companies for the first time. And I think what that kind of comes back to is this view that large platforms like Lightspeed arguably have an advantage because they have a chip stack, so to speak, to actually be able to capitalize these companies in ways that I think they need and others can't. And I think what's really interesting and sort of what drew me to Lightspeed was I feel like being on the right side of history right now is being at one of these large platforms who can kind of meet these really ambitious AI founders and their companies where they are and kind of be their partner of record all the way through, which I think has a lot of benefits.

43:20I would also say that I think there's increasingly clear room for specialists, meaning folks that specialize on a given stage or whether it's like focusing entirely on formation stage AI companies or helping build vertical AI companies or the like. It does seem like specialization and being more boutique is another avenue of really standing out in this industry that I think is becoming clear. And the reason for that is because now the founders have more options than they've ever had before, their ability to optimize what they're really looking for when they're building their company, especially at the early stages, is unparalleled.

43:50And so if I'm a founder starting a company today at Formation Stage, I have my choice between raising seed capital from a large platform that's going to be able to invest hundreds of millions of dollars to billions in my company over time and go all the way with me and have all the reach and resources of a global platform like Lightspeed. But I also can think about, hey, what if I want to go and raise seed funds from someone who all they do all day is build Formation Stage AI companies? And I think that's a really good thing for entrepreneurs that can be overwhelming at times. But I think what it says to me is that you're going to see this just like really extreme bifurcation in the industry.

44:19And this is not the first time that you've heard the term bifurcation thrown around in this context. The reason I bring it back up is not to say that it's novel, but more to say that I think it just seems like more of an inevitability to me than ever before, given these constraints that are going on in the industry. And so I believe in bifurcation. I believe that scale and specialization are the only two paths to really differentiating in this industry at this point. And then the other thing I'd say is, I just think because of how online our industry is now, it's increasingly becoming about who the partner is at the firm you're working with.

44:48And so typically what you see is that, sure, firms have brands, and I think that's never gonna go away. But I think individuals and their brands and what they're known for and how they're regarded by entrepreneurs does seem to carry a lot of weight in the industry. And I think that's a very good thing. And what I mean to say by it being a good thing is that I think now it's impossible for GPs to hide behind their firm's reputations. Like they have to be good investors. They have to be good partners and they have to really have strong reputations by doing good work and being good human beings with high integrity to the founders that they work with.

45:16And so I think when I think about like governing the industry and like good behavior of investors, I think that's a very good dynamic and one that I'm seeing play out. Yeah. So you mentioned there will be trillion dollar companies, multi-trillion dollar companies. Like what does that company look like? Well, I think the first candidate we could point to would be like whoever gets to win the AGI race is pretty obviously a trillion dollar company. And I think you could argue that whoever delivers AI in the most novel and sort of dominant way to consumers is a trillion dollar company. And today it looks a lot like open AI that's kind of has that has that pole position.

45:45One could imagine the company that owns Cogen and builds the most performant and widely used frontier model for building agents for software engineering is a trillion dollar company. It's not hard for me to see that. And today one might one might guess that's Anthropic, for example. And so I think these foundation model labs that are able to find a seam in the market on the enterprise or consumer side and build market-leading positions in those segments are trillion-dollar companies, in my humble opinion. I think if you can also look at some of these really vertically integrated companies going after industries like space or defense as like, I mean, just the GDP of these industries is just so massive that it's just hard for me to see how a company that doesn't come in and vertically integrate and completely upends the existing industry structure doesn't have a chance to scale into that type of outcome.

46:29And obviously, SpaceX seems well on its way, right? So those are a few examples. And look, I think the big question that an industry that we should always be asking ourselves is like, is there something systemic about the future that suggests that there will be more of these companies? Or are we just at some unique point in time where we're going to see five or six of them and then we're going to go back to the way things were, which was, you know, we used to get really excited about$20 billion companies. And now I think$20 billion is sort of a means to that higher end in a lot of cases. So that's what I don't know.

46:56And I'm humble about the fact that I don't know the answer to that. But if I were to take one side of it, I would say that there's definitely something systemic about the confluence of AI, about the confluence of having things like ubiquitous space travel, and some of these other fundamental capabilities that seem a little sci-fi at our fingertips. And the reason I get excited about that is like, yes, we see this set of companies being built right now that looks like they're going to be trillion-dollar companies. But I think what's more interesting is once these companies are household names and ubiquitous and essentially act as platforms for entrepreneurs to come in and build new companies on them, I think the power of these technologies is just so unparalleled that I can imagine, for example, that we're going to see AI agent companies that are worth a trillion dollars because they're they're not just automating existing labor spend, but they're creating like entirely new economies around the work that they can do that humans weren't capable of.

47:40Bucky, thank you so much. I feel way smarter about the state of infra and AI and venture. I'm sure the listeners and the viewers do as well. Really appreciate you doing this. Anytime. Thanks, partner. Thank you for listening to Generative Now. If you like this episode, please rate and review the show. And of course, subscribe. It really does help. And if you want to learn more, follow Lightspeed at LightspeedVP on X, YouTube, or LinkedIn. Generative Now is produced by Lightspeed in partnership with Pod People. I am Michael McNano, and we will be back next week. See you then.